CN112650195A - Equipment fault maintenance method and device based on cloud edge cooperation - Google Patents
Equipment fault maintenance method and device based on cloud edge cooperation Download PDFInfo
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0262—Confirmation of fault detection, e.g. extra checks to confirm that a failure has indeed occurred
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract
The invention discloses a method and a device for maintaining equipment faults based on cloud edge coordination, wherein the method comprises the following steps: acquiring historical operating data and real-time operating data of equipment; and sending the historical operating data to a cloud control center so that the cloud control center trains a fault prediction model according to the historical operating data, receiving the fault prediction model and the fault prediction application sent by the cloud control center by an edge computing center, analyzing the real-time operating data of the equipment by adopting the fault prediction model, generating an analysis result, and displaying the real-time operating data and the analysis result of the equipment by the fault prediction application. The embodiment of the invention can realize that: 1. the real-time performance of data acquisition and fault analysis is enhanced, and the maintenance difficulty of the robot is reduced; 2. the state prediction of the target equipment is realized at the edge, the waiting time of data uploading to a cloud computing platform is saved, and the time is saved for the predictive maintenance work of the industrial equipment.
Description
Technical Field
The invention relates to the technical field of communication, in particular to a method and a device for maintaining equipment faults based on cloud edge coordination.
Background
The internet of things is a network concept which extends and expands the user end of the internet to any article for information exchange and communication. It involves almost all important aspects of information technology.
The intelligent factory is the development trend of future factory, people just need in the office through the computer just can monitor the operating condition of various equipment to can control equipment and accomplish various tasks, can fix a position the trouble after the equipment breaks down rapidly, repair the trouble, make its resume production. Industrial robots, as an automated device to replace human work, are capable of performing a large number of tasks (e.g., welding, grinding, assembling, palletizing, and handling, etc.), which are also important in intelligent plants. Industrial robots in factories are connected through the technology of the internet of things, the working state of the industrial robots is monitored, controlled and diagnosed, and the intelligent industrial robot system has important significance for realizing intelligent factories.
Industrial robots require programmed control to operate and are inevitably subject to various malfunctions during operation due to faulty operation, environmental changes, imperfections in the control system of the robot itself, etc. When a fault occurs in the existing industrial robot, a professional maintenance worker can only detach the industrial robot on site to detect the fault, so that the real-time performance of data acquisition and fault analysis is poor, and the maintenance difficulty of the robot is increased.
Disclosure of Invention
In view of this, an object of the embodiments of the present invention is to provide a method and an apparatus for maintaining a device fault based on cloud edge coordination, which reduce the difficulty of robot maintenance.
In order to achieve the above object, an embodiment of the present invention provides an apparatus fault maintenance method based on cloud edge coordination, including:
acquiring historical operating data and real-time operating data of equipment;
sending the historical operating data to a cloud control center so that the cloud control center can train a fault prediction model according to the historical operating data and send the fault prediction model back to an edge computing center;
receiving a fault prediction model and a fault prediction application sent by the cloud control center;
analyzing the real-time operation data of the equipment by adopting the fault prediction model to generate an analysis result;
and displaying real-time operation data of the equipment and an analysis result generated by the fault prediction model by adopting the fault prediction application.
Preferably, the method comprises:
the edge calculation center is provided with an edge prediction module;
receiving a fault prediction model and a fault prediction application sent by the cloud control center, wherein the fault prediction model and the fault prediction application comprise the following steps:
the edge prediction module receives real-time operation data sent by the data acquisition module;
receiving a fault prediction model sent by the cloud control center,
and analyzing the real-time operation data of the equipment by adopting the fault prediction model to generate an analysis result.
Preferably, the method comprises:
wherein, the edge computing center is provided with a data acquisition module;
acquiring historical operating data and real-time operating data of equipment, wherein the method comprises the following steps:
the data acquisition module acquires historical operating data and real-time operating data of the equipment;
sending the historical operating data to a cloud control center so that the cloud control center trains a fault prediction model according to the historical operating data and sends the fault prediction model back to an edge computing center, and the method comprises the following steps:
the data acquisition module sends the historical operating data to a cloud control center;
and the data acquisition module sends the real-time operation data to the fault prediction application.
Preferably, after displaying the real-time operation data of the equipment and the analysis result generated by the fault prediction model by using the fault prediction application, the method further includes:
the fault prediction application is further configured to analyze at least one of the following indicators and display the indicator: the change of the real-time operation data, the equipment fault diagnosis condition, the health degree analysis and the residual life;
and when the at least one index is abnormal compared with the normal value, early warning is carried out on the potential fault of the equipment.
Preferably, the operational data of the device comprises at least one of: the total power consumption, the vibration of the base, the power and working current of each motor, the angular velocity of the rotary joint, the task execution result, the condition of the joint reducer, and the vibration signal data of the motor bearing.
Preferably, the apparatus comprises at least one of: industrial robots and numerically controlled machine tools.
The embodiment of the invention also provides an equipment fault maintenance method based on cloud edge coordination, which comprises the following steps:
receiving historical operating data of equipment sent by an edge computing center;
constructing a fault prediction model according to the historical operation data;
and issuing the fault prediction model and the fault prediction application to the edge computing center.
Preferably, the method comprises:
the cloud control center is provided with an AI training module;
receiving historical operating data of equipment sent by an edge computing center, wherein the historical operating data comprises:
the AI training module receives historical operation data of the equipment sent by the edge computing center;
constructing a fault prediction model according to the historical operating data, wherein the fault prediction model comprises the following steps:
the AI training module builds a fault prediction model according to the historical operating data;
the cloud control center is provided with a cloud edge collaborative computing module;
issuing the fault prediction model and the fault prediction application to the edge computing center, including:
the cloud edge collaborative computing module receives the fault prediction model constructed by the AI training module;
and issuing the fault prediction model and the fault prediction application to an edge computing center.
An embodiment of the present invention further provides an apparatus for maintaining an equipment fault based on cloud-edge coordination, including:
the acquisition module is used for acquiring historical operating data and real-time operating data of the equipment;
the sending module is used for sending the historical operating data to a cloud control center so that the cloud control center can train a fault prediction model according to the historical operating data and send the fault prediction model back to the edge computing center;
the receiving module is used for receiving the fault prediction model and the fault prediction application sent by the cloud control center;
the analysis module is used for analyzing the real-time operation data of the equipment by adopting the fault prediction model to generate an analysis result;
and the display module is used for displaying the real-time operation data of the equipment and the analysis result generated by the fault prediction model by adopting the fault prediction application.
An embodiment of the present invention further provides an apparatus for maintaining an equipment fault based on cloud-edge coordination, including:
the receiving module is used for receiving historical operating data of the equipment sent by the edge computing center;
the training module is used for training a fault prediction model according to the historical operation data;
and the issuing module is used for issuing the fault prediction model and the fault prediction application to the edge computing center.
Compared with the prior art, the embodiment of the invention has the following beneficial effects: according to the technical scheme of the embodiment of the invention, a fault prediction model is trained and constructed in a cloud control center according to historical operating data of equipment, the cloud control center issues the fault prediction model and fault prediction application to an edge computing center, then the edge computing center analyzes real-time operating data of the equipment according to the fault prediction model and generates an analysis result, and then the fault prediction application displays the analysis result; the invention can realize that: 1. professional maintenance personnel do not need to go to the site to perform disassembly detection on the equipment, so that the real-time performance of data acquisition and fault analysis is enhanced, and the maintenance difficulty of the equipment is reduced; 2. the abnormal conditions of the equipment are early discovered and treated, and then the efficiency, the utilization rate and the service life of the equipment are improved as much as possible through systematic maintenance; 3. the data in the service area is efficiently summarized through the near-end edge computing center, the state prediction of the target equipment is realized at the edge, the waiting time of uploading the data to the cloud control center is saved, and the predictive maintenance work of the equipment is captured in seconds.
Drawings
Fig. 1 is a flowchart of a first embodiment of a cloud edge coordination-based device fault maintenance method according to the present invention;
fig. 2 is a schematic diagram of an architecture among a cloud control center, an edge computing center, and a device side of the cloud-edge coordination-based device failure maintenance method according to the present invention;
fig. 3 is a flowchart of a second embodiment of the cloud edge coordination-based device fault maintenance method according to the present invention;
fig. 4 is a schematic diagram of a third embodiment of the cloud edge coordination-based device fault maintenance method according to the present invention;
fig. 5 is a schematic diagram of a first embodiment of the device for maintaining an equipment fault based on cloud edge coordination according to the present invention;
fig. 6 is a schematic diagram of a second embodiment of the cloud-edge-coordination-based device fault maintenance apparatus according to the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
At present, the industry of domestic industrial robots is rapidly developed, the use amount of industrial robots with various purposes is increased sharply, the range is enlarged continuously, and many traditional industries use industrial robots to replace manual work for production.
Aiming at key vulnerable part hosts, bearings, speed reducers and the like of large intelligent equipment such as industrial robots, numerical control machines and the like, the equipment is maintained and maintained manually and periodically, enterprises need to consume a large amount of manpower and material resources, the traditional after-the-fact maintenance and regular maintenance cannot meet the requirements of real-time, intelligent and networking maintenance and management of industrial equipment, and once the production line equipment is shut down unplanned, the production efficiency is greatly influenced.
The cloud edge coordination-based equipment fault maintenance method and device provided by the invention can facilitate a user to perform preventive maintenance on the industrial robot, monitor the real-time running state and conveniently troubleshoot and process potential faults of large intelligent equipment such as the industrial robot and a numerical control machine tool.
Fig. 1 is a flowchart of a first embodiment of a method for maintaining an equipment fault based on cloud edge coordination, as shown in fig. 1, the method for maintaining an equipment fault based on cloud edge coordination of this embodiment may specifically include the following steps:
s101, acquiring historical operating data and real-time operating data of the equipment.
Wherein the device comprises at least one of: industrial robots and numerically controlled machine tools. In the present embodiment, an example in which the apparatus is mainly an industrial robot will be mainly described.
As shown in fig. 2, the present embodiment can be structurally divided into an equipment layer, an edge layer and a cloud computing layer, wherein the equipment layer mainly includes an industrial robot and a gateway, the edge layer mainly includes a connection computing module, and the cloud computing layer mainly includes a cloud control center.
In the equipment layer, for example, the internet of things can be used for collecting the operation data of the industrial robot, and then the operation data is sent to the edge layer through the gateway. The industrial robot and the gateway send data based on MODBUS communication protocol and PROFINET standard.
Modbus is a serial communication protocol published by Modicon corporation (now Schneider Electric) in 1979 for communication using a Programmable Logic Controller (PLC). Modbus has become an industry standard (De factor) for industrial field communication protocols and is now a common connection between industrial electronic devices.
PROFINET was introduced by PROFIBUS International (PI) and is a new generation of automation bus standards based on industrial ethernet technology. PROFINET provides a complete network solution for the field of automation communication, which covers the hot topics of the current automation field, such as real-time ethernet, motion control, distributed automation, fail-safe and network security, and as a cross-provider technology, it is fully compatible with industrial ethernet and existing fieldbus (e.g. PROFIBUS) technologies, protecting the existing investment.
And S102, sending the historical operating data to a cloud control center so that the cloud control center can train a fault prediction model according to the historical operating data and send the fault prediction model back to an edge computing center.
In specific implementation, the cloud control center can adopt a deep learning algorithm to train and construct a fault prediction model
And S103, receiving the fault prediction model and the fault prediction application sent by the cloud control center.
The cloud control center is provided with a cloud edge collaborative computing module, and the cloud edge collaborative computing module sends the fault prediction model and the fault prediction application to the edge computing center.
And S104, analyzing the real-time operation data of the equipment by adopting the fault prediction model to generate an analysis result.
The results may include analysis prediction results of whether the industrial robot may or may not be abnormal, that is, whether a fault may occur, and the type of the fault.
And S105, displaying the real-time operation data of the equipment and the analysis result generated by the fault prediction model by adopting the fault prediction application.
Wherein the operational data of the device comprises at least one of: the total power consumption, the vibration of the base, the power and working current of each motor, the angular velocity of the rotary joint, the task execution result, the condition of the joint reducer, and the vibration signal data of the motor bearing.
In other embodiments of the invention, the operational data may also include the state of the joint reducer of the industrial robot. The health condition of the industrial robot reducer is comprehensively analyzed through three aspects of state overview, real-time operation data, alarm record and the like of the robot joint reducer, the predictive diagnosis of the industrial robot joint reducer is realized, accurate prediction is made before the fault affects the normal production of the industrial robot, and a corresponding diagnosis solution is provided in time.
In other embodiments of the invention, the operation data may further include acceleration of a motor bearing, which is a key component of the industrial robot, and a fault prediction model is established based on a deep learning algorithm to diagnose faults and perform preventive maintenance, so as to provide maintenance suggestions for field maintenance personnel. When a fault prediction model is constructed, historical operation data sent to a cloud computing control center by an edge computing center comprises vibration signal data of a motor bearing, and then the fault detection model analyzes real-time operation data of the robot, analyzes whether the bearing has a fault and provides a prediction result of the fault type.
The embodiment of the invention has the following beneficial effects: according to the technical scheme of the embodiment of the invention, a fault prediction model is trained and constructed in a cloud control center according to historical operating data of equipment, the cloud control center issues the fault prediction model and fault prediction application to an edge computing center, then the edge computing center analyzes real-time operating data of the equipment according to the fault prediction model and generates an analysis result, and then the fault prediction application displays the analysis result; the invention can realize that: 1. professional maintenance personnel do not need to go to the site to perform disassembly detection on the robot, so that the real-time performance of data acquisition and fault analysis is enhanced, and the maintenance difficulty of the robot is reduced; 2. the data in the service area are efficiently summarized through the near-end edge computing center, the state prediction of the target equipment is realized at the edge, the waiting time of data uploading to a cloud computing platform is saved, and the working time of predictive maintenance of the industrial equipment is captured by seconds.
In the following, in the second embodiment, the method for maintaining the device fault based on cloud edge coordination according to the present invention will be described in further detail.
Wherein, the edge computing center is provided with an edge prediction module;
specifically, step S103 includes the following steps: a, the edge prediction module receives real-time operation data sent by a data acquisition module; and B, the edge prediction module receives a fault prediction model sent by the cloud control center so as to analyze the real-time operation data of the equipment by adopting the fault prediction model and generate an analysis result.
In this embodiment, an edge prediction module in an edge computing center performs fault judgment on equipment on an equipment side, and is a core link of an application solution for preventive maintenance of intelligent factory equipment. The edge prediction module receives a fault prediction model sent by the cloud control center, takes the real-time operation data of the equipment acquired by the data acquisition module as input, performs equipment fault predictive analysis on the equipment at the equipment end by using the fault prediction model, outputs a predictive maintenance result of the equipment, and provides the result data for fault prediction Application through an open fault diagnosis API (Application Programming Interface).
Wherein, the edge computing center is provided with a data acquisition module;
specifically, the step S101 includes: and C, the data acquisition module acquires historical operating data and real-time operating data of the equipment.
The gateway of the equipment end acquires historical operating data and real-time operating data of the industrial robot, and then performs protocol conversion on the data and uploads the data to the data acquisition module. In this embodiment, the gateway communicates with the data collection interface via the HTTP protocol.
It should be noted that, in conjunction with fig. 2, the real-time operation data of the industrial robot, including the operation data and the state data of the industrial robot, collected by the data collection module in real time, facilitates the monitoring of the operation of the industrial robot and the monitoring of the operation state of the industrial robot. The data acquisition module only accepts data uploaded through an HTTP protocol, and for industrial robots of MODBUS and PROFINET protocols, edge end data acquisition needs to be carried out through an Internet of things gateway, and the MODBUS and PROFINET protocols are converted into the HTTP protocol. The industrial robot with the HTTP protocol can directly communicate with the data acquisition module to upload equipment data at the equipment end.
The data acquisition interface is used as a data center for predictive maintenance of equipment data on the equipment side, has the functions of data acquisition, data storage and data uploading, is temporarily stored on the edge side after the equipment data are acquired, and transmits real-time operation data to an edge prediction module and fault prediction application on the edge side and uploads historical operation data to a cloud control center on the one hand aiming at the acquired equipment data.
Step S102 includes: d, the data acquisition module sends the historical operation data to a cloud control center; and E, the data acquisition module sends the real-time operation data to the fault prediction application.
The failure prediction application is a data display link of the embodiment of the invention, and the displayed data mainly comprises real-time operation data of equipment, failure diagnosis of equipment ledger equipment, health degree analysis, residual life and the like. In this embodiment, the device is an industrial robot, and therefore the fault prediction application displays real-time operation data of the industrial robot, fault diagnosis of the device ledger device, health degree analysis, remaining life and the like. Here, the failure prediction application receives the real-time operation data sent by the data acquisition module and the analysis result sent by the edge prediction module, and further performs analysis based on the analysis result to know whether the industrial robot is likely to fail, the health degree and the remaining life of the industrial robot, and the like. The fault prediction application is issued and deployed by a cloud edge cooperative computing module of a cloud control center, and application management is performed in the cloud control center.
The issuing mode can be an active issuing mode or a passive response mode.
As shown in fig. 3, after step S105, the method further includes:
s201, the failure prediction application is further used for analyzing at least one of the following indexes and displaying the indexes: the change of the real-time operation data, the equipment fault diagnosis condition, the health degree analysis and the residual life;
s202, when the at least one index is abnormal compared with the normal value, early warning is carried out on the potential fault of the equipment.
Specifically, when the analysis result shows that the industrial robot may be abnormal (or may be in failure), a sound or text warning message is sent. For example, a normal value range of each index value when the industrial robot normally operates may be set in advance, and if the analysis result of the industrial robot shows which index value is out of the normal value range or is in a critical value of the normal value range, the warning information may be displayed in a text manner or may be transmitted in a sound manner
The embodiment of the invention is based on a cloud edge collaborative computing method, a cloud computing layer and an edge layer are linked, the requirements of customers on remote control, data processing and intelligent application of edge resources are met, and an equipment fault prediction model and a fault prediction application are issued to an edge computing center; the edge computing center runs the fault prediction application, so that not only can the equipment fault be diagnosed in real time, but also preventive maintenance can be carried out; on the other hand, the potential fault or the fault which has occurred can be pre-warned in a text or sound mode, so that the equipment maintenance efficiency is improved, and the maintenance complexity is reduced.
Fig. 4 is a flowchart of a third embodiment of the cloud edge coordination-based device fault maintenance method according to the present invention. As shown in fig. 4, the method for maintaining an equipment fault based on cloud edge coordination according to this embodiment may specifically include the following steps:
s301, receiving historical operating data of the equipment sent by the edge computing center.
Wherein, the cloud control center is provided with an AI (Artificial Intelligence) training module.
The step S301 includes: and F, the AI training module receives the historical operation data of the equipment sent by the edge computing center.
S302, a fault prediction model is built according to the historical operation data.
In specific implementation, a plurality of groups of sample data of the equipment can be collected firstly; the sample data comprises positive sample data of the equipment in a normal operation state and/or negative sample data of the equipment in an abnormal operation state; secondly, marking the operation state type of the equipment corresponding to each group of sample data; the operation state type comprises a normal operation state and/or an abnormal operation state; and finally, inputting each group of sample data and the corresponding operating state type into a fault prediction model for training, thereby obtaining a trained fault prediction model for determining the operating state type based on the sample data.
The step S302 includes: and G, the AI training module constructs a fault prediction model according to the historical operation data.
And S303, issuing the fault prediction model and the fault prediction application to the edge computing center.
The cloud control center is provided with a cloud edge collaborative computing module;
the step S303 includes: h, the cloud edge cooperative computing module receives the fault prediction model constructed by the AI training module; and I, issuing the fault prediction model and the fault prediction application to an edge computing center.
The embodiment of the invention has the following beneficial effects: according to the technical scheme of the embodiment of the invention, a fault prediction model is trained and constructed in a cloud control center according to historical operating data of equipment, the cloud control center issues the fault prediction model and fault prediction application to an edge computing center, then the edge computing center analyzes real-time operating data of the equipment according to the fault prediction model and generates an analysis result, and then the fault prediction application displays the analysis result; the invention can realize that: 1. professional maintenance personnel do not need to go to the site to perform disassembly detection on the robot, so that the real-time performance of data acquisition and fault analysis is enhanced, and the maintenance difficulty of the robot is reduced; 2. the data in the service area are efficiently summarized through the near-end edge computing center, the state prediction of the target equipment is realized at the edge, the waiting time of data uploading to a cloud computing platform is saved, and the working time of predictive maintenance of the industrial equipment is captured by seconds.
Fig. 5 is a schematic diagram of a first embodiment of the cloud-edge-coordination-based device fault maintenance apparatus according to the present invention. As shown in fig. 5, the apparatus for maintaining an equipment fault based on cloud-edge coordination according to this embodiment may specifically include an obtaining module 501, a sending module 502, a receiving module 503, an analyzing module 504, and a displaying module 505.
An obtaining module 501, configured to obtain historical operating data and real-time operating data of a device;
a sending module 502, configured to send the historical operating data to a cloud control center, so that the cloud control center trains a fault prediction model according to the historical operating data, and sends the fault prediction model back to an edge computing center;
a receiving module 503, configured to receive the fault prediction model and the fault prediction application sent by the cloud control center;
an analysis module 504, configured to analyze the real-time operating data of the device using the fault prediction model to generate an analysis result;
and a display module 505, configured to display the real-time operation data of the device and an analysis result generated by the fault prediction model by using the fault prediction application.
The device for maintaining an equipment fault based on cloud edge coordination in this embodiment is an embodiment of a device corresponding to the method for maintaining an equipment fault based on cloud edge coordination in the first embodiment, and an implementation mechanism for implementing the equipment fault maintenance based on cloud edge coordination by using the modules is the same as that of the method for maintaining an equipment fault based on cloud edge coordination in the embodiment shown in fig. 1, and details of the method for maintaining an equipment fault based on cloud edge coordination in the embodiment shown in fig. 1 may be referred to, and are not described herein again.
Fig. 6 is a schematic diagram of a first embodiment of the cloud-edge-coordination-based device fault maintenance apparatus according to the present invention. As shown in fig. 6, the apparatus for maintaining an equipment fault based on cloud-edge coordination according to this embodiment may specifically include a receiving module 601, a training module 602, and a sending module 603.
A receiving module 601, configured to receive device history operation data sent by an edge computing center;
a training module 602, configured to train a fault prediction model according to the historical operating data;
and the issuing module 603 is configured to issue the fault prediction model and the fault prediction application to the edge computing center.
The device for maintaining an equipment fault based on cloud-edge coordination in this embodiment is an embodiment of a device corresponding to the method for maintaining an equipment fault based on cloud-edge coordination described in the third embodiment, and an implementation mechanism for implementing the equipment fault maintenance based on cloud-edge coordination by using the modules is the same as the implementation mechanism of the method for maintaining an equipment fault based on cloud-edge coordination in the embodiment shown in fig. 4, and reference may be made to the description of the embodiment shown in fig. 4 in detail, which is not described herein again.
The above embodiments are only exemplary embodiments of the present invention, and are not intended to limit the present invention, and the scope of the present invention is defined by the claims. Various modifications and equivalents may be made by those skilled in the art within the spirit and scope of the present invention, and such modifications and equivalents should also be considered as falling within the scope of the present invention.
Claims (10)
1. A device fault maintenance method based on cloud edge coordination is characterized by comprising the following steps:
acquiring historical operating data and real-time operating data of equipment;
sending the historical operating data to a cloud control center so that the cloud control center can train a fault prediction model according to the historical operating data and send the fault prediction model back to an edge computing center;
receiving a fault prediction model and a fault prediction application sent by the cloud control center;
analyzing the real-time operation data of the equipment by adopting the fault prediction model to generate an analysis result;
and displaying real-time operation data of the equipment and an analysis result generated by the fault prediction model by adopting the fault prediction application.
2. The method of claim 1, comprising:
wherein, the edge computing center is provided with an edge prediction module;
receiving a fault prediction model and a fault prediction application sent by the cloud control center, wherein the fault prediction model and the fault prediction application comprise the following steps:
the edge prediction module receives real-time operation data sent by the data acquisition module;
the edge prediction module receives a fault prediction model sent by the cloud control center,
and the edge prediction module analyzes the real-time operation data of the equipment by adopting the fault prediction model to generate an analysis result.
3. The method of claim 1, comprising:
wherein, the edge computing center is provided with a data acquisition module;
acquiring historical operating data and real-time operating data of equipment, wherein the method comprises the following steps:
the data acquisition module acquires historical operating data and real-time operating data of the equipment;
sending the historical operating data to a cloud control center so that the cloud control center trains a fault prediction model according to the historical operating data and sends the fault prediction model back to an edge computing center, and the method comprises the following steps:
the data acquisition module sends the historical operating data to a cloud control center;
and the data acquisition module sends the real-time operation data to the fault prediction application.
4. The method of claim 1, wherein after displaying real-time operational data of the equipment and analysis results generated by the fault prediction model using the fault prediction application, the method further comprises:
the fault prediction application is further configured to analyze at least one of the following indicators and display the indicator: the change of the real-time operation data, the equipment fault diagnosis condition, the health degree analysis and the residual life;
and when the at least one index is abnormal compared with the normal value, early warning is carried out on the potential fault of the equipment.
5. The method of claim 1, wherein the operational data of the device comprises at least one of: the total power consumption, the vibration of the base, the power and working current of each motor, the angular velocity of the rotary joint, the task execution result, the condition of the joint reducer, and the vibration signal data of the motor bearing.
6. The method of claim 1, wherein the device comprises at least one of: industrial robots and numerically controlled machine tools.
7. A device fault maintenance method based on cloud edge coordination is characterized by comprising the following steps:
receiving historical operating data of equipment sent by an edge computing center;
constructing a fault prediction model according to the historical operation data;
and issuing the fault prediction model and the fault prediction application to the edge computing center.
8. The method of claim 1, comprising:
the cloud control center is provided with an AI training module;
receiving historical operating data of equipment sent by an edge computing center, wherein the historical operating data comprises:
the AI training module receives historical operation data of the equipment sent by the edge computing center;
constructing a fault prediction model according to the historical operating data, wherein the fault prediction model comprises the following steps:
the AI training module builds a fault prediction model according to the historical operating data;
the cloud control center is provided with a cloud edge collaborative computing module;
issuing the fault prediction model and the fault prediction application to the edge computing center, including:
the cloud edge collaborative computing module receives the fault prediction model constructed by the AI training module;
and issuing the fault prediction model and the fault prediction application to an edge computing center.
9. An equipment fault maintenance device based on cloud edge coordination is characterized by comprising:
the acquisition module is used for acquiring historical operating data and real-time operating data of the equipment;
the sending module is used for sending the historical operating data to a cloud control center so that the cloud control center can train a fault prediction model according to the historical operating data and send the fault prediction model back to the edge computing center;
the receiving module is used for receiving the fault prediction model and the fault prediction application sent by the cloud control center;
the analysis module is used for analyzing the real-time operation data of the equipment by adopting the fault prediction model to generate an analysis result;
and the display module is used for displaying the real-time operation data of the equipment and the analysis result generated by the fault prediction model by adopting the fault prediction application.
10. An equipment fault maintenance device based on cloud edge coordination is characterized by comprising: the receiving module is used for receiving historical operating data of the equipment sent by the edge computing center;
the training module is used for training a fault prediction model according to the historical operation data;
and the issuing module is used for issuing the fault prediction model and the fault prediction application to the edge computing center.
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| CN202011487135.7A CN112650195A (en) | 2020-12-16 | 2020-12-16 | Equipment fault maintenance method and device based on cloud edge cooperation |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113341813A (en) * | 2021-06-11 | 2021-09-03 | 上海天麦能源科技有限公司 | Urban gas medium-low pressure pipe network detection method and system |
| CN114781762A (en) * | 2022-06-21 | 2022-07-22 | 四川观想科技股份有限公司 | Equipment fault prediction method based on life consumption |
| CN117172758A (en) * | 2023-11-02 | 2023-12-05 | 启东茂济医药科技有限公司 | Cloud edge fusion-based drug production line equipment fault detection method and system |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113341813A (en) * | 2021-06-11 | 2021-09-03 | 上海天麦能源科技有限公司 | Urban gas medium-low pressure pipe network detection method and system |
| CN114781762A (en) * | 2022-06-21 | 2022-07-22 | 四川观想科技股份有限公司 | Equipment fault prediction method based on life consumption |
| CN114781762B (en) * | 2022-06-21 | 2022-09-23 | 四川观想科技股份有限公司 | Equipment fault prediction method based on life consumption |
| CN117172758A (en) * | 2023-11-02 | 2023-12-05 | 启东茂济医药科技有限公司 | Cloud edge fusion-based drug production line equipment fault detection method and system |
| CN117172758B (en) * | 2023-11-02 | 2024-03-08 | 启东茂济医药科技有限公司 | Cloud edge fusion-based drug production line equipment fault detection method and system |
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